Rick Janssen

Cyclic incrementality in competitive coevolution: Evolvability through pseudo-baldwinian switching-genes

Coevolving systems are notoriously difficult to understand. This is largely due to the Red Queen effect that dictates heterospecific fitness interdependence. In simulation studies of coevolving systems, master tournaments are often used to obtain more informed fitness measures by testing evolved individuals against past and future opponents. However, such tournaments still contain certain ambiguities. We introduce the use of a phenotypic cluster analysis to examine the distribution of opponent categories throughout an evolutionary sequence. This analysis, adopted from widespread usage in the bioinformatics community, can be applied to master tournament data. This allows us to construct behavior-based category trees, obtaining a hierarchical classification of phenotypes that are suspected to interleave during cyclic evolution. We use the cluster data to establish the existence of switching-genes that control opponent specialization, suggesting the retention of dormant genetic adaptations, that is, genetic memory. Our overarching goal is to reiterate how computer simulations may have importance to the broader understanding of evolutionary dynamics in general. We emphasize a further shift from a component-driven to an interaction-driven perspective in understanding coevolving systems. As yet, it is unclear how the sudden development of switching-genes relates to the gradual emergence of genetic adaptability. Likely, context genes gradually provide the appropriate genetic environment wherein the switching-gene effect can be exploited.

Agent model reveals the influence of vocal tract anatomy on speech during ontogeny and glossogeny

This study introduces a new model for the investigation of the complex manner in which vocal tract anatomy affects human speech production and may influence language change and evolution. The anatomy of the human vocal tract has long been recognized to play a crucial role in speech production and patterning (Fant, 1971; Ohala, 1983). It imposes discrete relations between articulatory parameters and acoustics (Stevens & Keyser, 2010), with highly nonlinear mappings between them (Stevens, 1968, 1989), and it has been recently suggested that inter-individual and patterned inter-population variation in the anatomy of the vocal tract might play a role in explaining patterns of linguistic diversity (Dediu, Janssen, & Moisik, 2017). We investigate these complex relationships by instructing a computersimulated agent to learn to reproduce, as well as possible, target speech sounds by controlling the articulators of a detailed 3D geometric model of the human vocal tract based on the VocalTractLab 2.1 (Birkholz, Jackèl, & Kroger, 2006), modified to allow changes in larynx height and hard palate shape. More precisely, the agent minimizes the Euclidean distance (in the F1–F5 formant space) between the target and the produced sounds using a genetic algorithm that optimizes the synaptic weights of a neural network that maps formants to articulatory parameter values1. Here, we apply this model to two case studies, both using the five-vowel system [a], [æ], [i], [u], and [@], but investigating the effects of variation in different components of the vocal tract. In the first case study, we revisit the debate concerning the role of larynx height in human speech, which has important implications for the evolution of speech

Modelling human hard palate shape with Bézier curves.

People vary at most levels, from the molecular to the cognitive, and the shape of the hard palate (the bony roof of the mouth) is no exception. The patterns of variation in the hard palate are important for the forensic sciences and (palaeo)anthropology, and might also play a role in speech production, both in pathological cases and normal variation. Here we describe a method based on Bézier curves, whose main aim is to generate possible shapes of the hard palate in humans for use in computer simulations of speech production and language evolution. Moreover, our method can also capture existing patterns of variation using few and easy-to-interpret parameters, and fits actual data obtained from MRI traces very well with as little as two or three free parameters. When compared to the widely-used Principal Component Analysis (PCA), our method fits actual data slightly worse for the same number of degrees of freedom. However, it is much better at generating new shapes without requiring a calibration sample, its parameters have clearer interpretations, and their ranges are grounded in geometrical considerations.